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Unit 3: Representation of Knowledge

Introduction Notes

In the previous unit, we dealt with the concepts of knowledge progression and model, importance
of knowledge, characteristics and structure of characteristics of KBS.

Knowledge Representation (KR) is an area of artificial intelligence research aimed at representing
knowledge in symbols to facilitate inferencing from those knowledge elements, creating new
elements of knowledge. The KR can be made to be independent of the underlying knowledge
model or KBS such as a semantic network.

Knowledge Representation (KR) research involves analysis of how to reason accurately and
effectively and how best to use a set of symbols to represent a set of facts within a knowledge
domain. A symbol vocabulary and a system of logic are combined to enable inferences about
elements in the KR to create new KR sentences. Logic is used to supply formal semantics of how
reasoning functions should be applied to the symbols in the KR system. Logic is also used to
define how operators can process and reshape the knowledge. Examples of operators and
operations include negation, conjunction, adverbs, adjectives, quantifiers and modal operators.
The logic is interpretation theory. These elements – symbols, operators, and interpretation
theory – are what give sequences of symbols meaning within a KR.
A key parameter in choosing or creating a KR is its expressivity. The more expressive a KR, the
easier and more compact it is to express a fact or element of knowledge within the semantics and
grammar of that KR. However, more expressive languages are likely to require more complex
logic and algorithms to construct equivalent inferences. A highly expressive KR is also less
likely to be complete and consistent. Less expressive KRs may be both complete and consistent.
Autoepistemic temporal modal logic is a highly expressive KR system, encompassing meaningful
chunks of knowledge with brief, simple symbol sequences (sentences). Propositional logic is
much less expressive but highly consistent and complete and can efficiently produce inferences
with minimal algorithm complexity. Nonetheless, only the limitations of an underlying
knowledge base affect the ease with which inferences may ultimately be made (once the
appropriate KR has been found). This is because a knowledge set may be exported from a
knowledge model or KBS into different KRs, with different degrees of expressiveness,
completeness, and consistency. If a particular KR is inadequate in some way, that set of
problematic KR elements may be transformed by importing them into a KBS, modified and
operated on to eliminate the problematic elements or augmented with additional knowledge
imported from other sources, and then exported into a different, more appropriate KR. In this
unit, you will understand the concepts of knowledge representation techniques along with the
knowledge organization, manipulation and acquisition.

3.1 Knowledge Representation Techniques

In applying KR systems to practical problems, the complexity of the problem may exceed the
resource constraints or the capabilities of the KR system. Recent developments in KR include the
concept of the Semantic Web, and development of XML-based knowledge representation
languages and standards, including Resource Description Framework (RDF), RDF Schema, Topic
Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer (OIL), and Web
Ontology Language (OWL).
There are several KR techniques such as frames, rules, tagging, and semantic networks which
originated in cognitive science. Since knowledge is used to achieve intelligent behavior, the
fundamental goal of knowledge representation is to facilitate reasoning, inferencing, or drawing
conclusions. A good KR must be both declarative and procedural knowledge. What is knowledge

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